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Automate Canva from an agent — via the AI Connector MCP server (generate, find, edit, export designs in natural language) or the Connect REST API (OAuth 2.0 + PKCE, brand template autofill, asset uploads, exports, folders, comments). Use when: connecting an agent to Canva, batch-producing social posts or decks from a brand template and a spreadsheet, exporting designs to PDF/PNG/PPTX/MP4, uploading assets into a Canva account, building a Canva integration, or when the user mentions "Canva", "brand template", "autofill", "Connect API", or "mcp.canva.com".

日本語の概要は準備中です。原文の説明を表示しています。

Atuin replaces your shell history file with a searchable SQLite database that records directory, exit code, duration and host for every command, and can sync it end-to-end encrypted between machines. Use when a user asks to "search shell history", "replace Ctrl+R", "sync history across machines", "find the command I ran in this directory", "import bash/zsh history", "see my most used commands" or "self-host Atuin".

日本語の概要は準備中です。原文の説明を表示しています。

Writes the App Store "What's New in This Version" text for an app update from the repository's commit history: finds the range since the last shipped version, separates changes users can notice from internal work, drafts plain-text notes within Apple's 4,000-character limit and review guidelines, and returns a trace from every sentence back to its commits. Use when someone asks to "write release notes for the App Store", "generate What's New from git", "summarize changes since the last tag for the store listing", or needs the same notes cut to Google Play's 500 characters.

日本語の概要は準備中です。原文の説明を表示しています。

Reconstructs the workflow behind a past artifact by querying local ax session, recall, and commit history data. Use when a user asks "what made X work", "how was Y shipped/built", "extract workflow from <date|sha>", "workflow around <topic>", "recipe for this feature", or similar. It finds the anchor session or commit, inspects ax sessions, ax recall, and ax sessions show, and returns an ordered read-only narrative with decisions and evidence.

日本語の概要は準備中です。原文の説明を表示しています。

Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.

日本語の概要は準備中です。原文の説明を表示しています。

Generates and executes load test scripts for APIs using k6, wrk, or autocannon. Creates realistic test scenarios from OpenAPI specs, route files, or endpoint descriptions. Use when someone needs to load test, stress test, benchmark, or find the breaking point of their API. Trigger words: load test, stress test, benchmark, RPS, concurrent users, breaking point, performance test, k6, wrk.

日本語の概要は準備中です。原文の説明を表示しています。

Audit web pages and components for WCAG 2.2 accessibility compliance. Use when a user asks to check accessibility, find a11y issues, audit for WCAG compliance, fix screen reader problems, check color contrast, ensure keyboard navigation works, or prepare for accessibility regulations like the European Accessibility Act or ADA.

日本語の概要は準備中です。原文の説明を表示しています。

Core marketing principles for startups. Covers positioning, messaging, channels, and growth. Focus on what works for resource-constrained teams. Battle scars included: I've watched startups burn $1M on Instagram ads because "that's where competitors are", seen "growth hacks" destroy brand trust overnight, and advised founders who scaled marketing before finding product-market fit. Contrarian take: Most startup marketing advice is backwards. The best marketing doesn't feel like marketing. Distribution comes before product features. And your first 100 customers should never come from paid ads. Use when "marketing strategy, positioning, messaging, customer acquisition, growth channels, brand, go-to-market, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Expert at making systems observable and debuggable. Covers structured logging, metrics collection, distributed tracing, error tracking, and alerting. Knows how to find the needle in the haystack when production breaks at 3 AM. Use when "observability, logging, metrics, tracing, monitoring, error tracking, Sentry, Datadog, OpenTelemetry, debugging production, observability, logging, metrics, tracing, monitoring, sentry, prometheus, opentelemetry" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Master the AI tools that transform hiring and HR operations. From sourcing candidates to onboarding, find and retain great talent faster. Use when "hiring, recruiting, HR, talent, performance management, hr, recruiting, hiring, talent, people" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Most startup ideas are bad. Not because founders lack creativity, but because they're solving invented problems instead of lived ones. Paul Graham's essays reveal a pattern: the best ideas come from living in the future and noticing what's missing. Schlep blindness hides gold. Organic ideas beat brainstormed ones. Frighteningly ambitious ideas are often easier. This skill synthesizes PG's idea-finding wisdom into actionable guidance. Use when "keywords, contexts, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in understanding and navigating legacy codebases. Covers code archaeology techniques, finding hidden knowledge, mapping dependencies, and extracting understanding from code without documentation. Knows how to read the stories that old code tells. Use when "legacy code, old codebase, understand this code, archaeology, what does this do, no documentation, ancient code, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in designing hidden features, secret codes, and delightful surprises in products. Covers discovery mechanics, reward calibration, shareability triggers, and maintaining the magic. Knows how to create moments that make users feel special for finding them. Use when "easter egg, hidden feature, secret code, surprise and delight, konami code, hidden message, secret mode, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

The art and science of narrative-driven brand communication. This skill transforms brand messages into compelling stories that connect emotionally, build memory, and drive action. In a world of infinite content, story is the only thing that cuts through. Great brands don't sell products—they tell stories. Apple tells the story of creative rebels. Nike tells the story of human potential. Patagonia tells the story of environmental stewardship. This skill teaches you to find, craft, and tell your brand's story across every touchpoint. AI amplifies storytelling capability—generating variations, adapting across channels, maintaining consistency—but the human insight that identifies the true story remains essential. This skill bridges narrative craft with AI execution. Use when "story, storytelling, narrative, brand story, origin story, hero journey, emotional connection, customer story, testimonial, case study, brand film, about us, storytelling, narrative, brand-story, emotional, hero-journey, content" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Triage Meetings/_inbox/ — move each file to the correct subfolder by finding adjacent/similar notes via qmd, applying learned routing conventions from memory, and confirming with the user.

日本語の概要は準備中です。原文の説明を表示しています。

Find local-business SEO prospects in a single command. Searches Google for "<niche> <city>", filters out aggregators (Yelp, Yellow Pages, Angi), enriches each remaining domain with DataForSEO Labs domain overview, On-Page technical audit, and Backlinks summary in parallel, then ranks the businesses by a closeability score (0-100) — highest scores are the easiest to land as SEO clients.

日本語の概要は準備中です。原文の説明を表示しています。

Find keywords competitors rank for that you don't, via the DataForSEO Labs domain_intersection endpoint. Surfaces the highest-leverage content opportunities sorted by search volume and the competitor's ranking position, with cross-competitor consensus signals.

日本語の概要は準備中です。原文の説明を表示しています。

Draft a personalized cold email to a prospect based on the SEO audit findings. Reads the audit JSON for the given domain, picks the 2-3 most impactful issues, and writes an outreach email that references specific data (current ranking, traffic value, named issues) so it doesn't look like spam.

日本語の概要は準備中です。原文の説明を表示しています。

Generate a markdown SEO report deliverable from a saved DataForSEO-powered audit. Produces a client-ready markdown document with executive summary, scores, key findings, prioritized actions, and supporting data tables — ready to send to a client.

日本語の概要は準備中です。原文の説明を表示しています。

Finds and finishes the last 20% of a built solution — the experiential layer agents skip: what the output actually reads like, which pages or screens should exist, first-run and empty states, opinionated defaults, the golden path. Use when planning any build so the magic gets specced as concretely as the plumbing, before calling any build "done", when a solution works but feels like a demo, or when the user says "last 20%", "finish this", "make it actually good", "is this actually done", or "why does this feel flat".

日本語の概要は準備中です。原文の説明を表示しています。

Keeps specification documents and code in agreement. Audit mode finds every divergence when an implementation is claimed complete; sync mode detects spec drift in staged changes and updates the spec after user approval, so each commit is a reconciled snapshot of spec, tests, and code. Use when an implementation is claimed complete against a spec, before committing changes that may drift from the spec, or after approving decisions that must flow back into it.

日本語の概要は準備中です。原文の説明を表示しています。

Challenges a recommendation, design, large change, or any artifact submitted for hard review (spec, proposal, policy, plan) across 5 dimensions (consistency, completeness, actionability, alignment, risk), from the standards of a named senior expert in the artifact's domain and ideally from a fresh context or a different model. Searches assuming defects exist and reports only those that survive evidence. Panel mode runs several independent lenses in parallel on a release candidate, verifies every finding against the current head, and routes confirmed ones to file owners. Use before shipping a significant recommendation, design, large branch, or multi-agent release, or when asked to tear something apart, be brutally honest, or poke holes in it.

日本語の概要は準備中です。原文の説明を表示しています。

Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass inventories artifacts, derives the observability level (L0 PDF-only / L1 +LaTeX / L2 +repo+results) by fixed rule, and extracts span-anchored, hashed, checkable claims (numbers, comparisons, scope, method, baselines, citations, captions, table cells) into claims.json. An OPTIONAL additive cross-model pass ADDS span-anchored semantic claims — method, theorem statements with their assumptions, definitions, proof/derivation steps and equations, scope, baselines, conclusions, the motivation span, and reproducibility-artifact references (the proof, derivation, and structure anchors the family B/D/G auditors need) — it never invents a number, emits a finding, or computes a verdict. Run FIRST, before any audit skill. Triggers: "build the ledger", "extract claims", "prep for integrity audit", "evidence ledger", "建证据账本".

日本語の概要は準備中です。原文の説明を表示しています。